class_docstr: A class representing a mean scaling approach. Inputs are simply rescaled
  based on their mean.
  
  Parameters
  ----------
  minimum_scale
      The minimum value to which re-scaled values will be clipped to.
      (default: 1e-10)
  clip_max
      The maximum value to which re-scaled values will be clipped to.
      Negative values will be clipped at -clip_max and positive values at
      clip_max. (default: None)
code: def hybrid_forward(
      self,
      F,
      data: Tensor,
      observed_indicator: Tensor,
      scale: Optional[Tensor],
      rep_params: List[Tensor],
      **kwargs,
  ) -> Tuple[Tensor, Tensor, List[Tensor]]:
      data = F.cast(data, dtype="float32")
  
      if scale is None:
          scale = self.compute_scale(F, data, observed_indicator)
          scale = scale.expand_dims(axis=1)
  
      scaled_data = F.broadcast_div(data, scale)
  
      if self.clip_max is not None:
          scaled_data = F.clip(
              scaled_data, -self.clip_max, self.clip_max
          )
  
      return scaled_data, scale, []
  
file_path: gluonts/src/gluonts/mx/representation/mean_scaling.py
imports: from typing import List, Optional, Tuple
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .representation import Representation
label_desc: Use image node_id 3 for calling the MeanScaling obj's underlying member method code with example usage: obj.hybrid_forward(F, data, observed_indicator, scale, rep_params) and returns: scaled_data,  scale,  list